Financial applications focusing on portfolio management for Python
Project description
PyFinanceLab
PyFinanceLab is a project which brings together various financial applications into one package for research and portfolio management. PyFinanceLab is in pre-alpha development. Please open an issue if you find any bugs.
Features
PyFinanceLab is a library of financial applications focusing on portfolio management for Python.
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Data Api Wrapper
The data api wrapper makes it easy to switch between yfinance (free to use) and tia (Bloomberg Professional Service subscription required) libraries for pulling financial data.
Installation Instructions
Create a new virtual environment in Anaconda Prompt.
conda create -n pyfinlab python=3.8
Activate new environment.
conda activate pyfinlab
Install the latest version using pip.
pip install pyfinlab
Install the necessary dependencies from requirements.txt.
conda install --file requirements.txt
Roadmap
Future development will include:
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Classification Schema
Classify an investment universe of tickers into specified categories such as sector, size, or value.
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Constraints Modeling
Automatically generate weight constraints for a universe of tickers.
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Risk Modeling
Sample, test, and select the best risk model for generating covariance matrices for input into portfolio optimizers such as mean-variance optimization (MVO). Examples include empirical covariance, ledoit-wolf shrinkage, minimum covariance determinant, and more.
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Portfolio Optimization
Utilize the classification schema, constraints modeling, risk modeling, and return modeling to optimize a portfolio of assets.
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Portfolio Backtesting
Backtest portfolios and generate performance graphical plots and statistics.
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Report Generation
Report results in a nicely formatted and easily readable Excel file.
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Documentation
Documentation will be published as this Python library is further developed.
Project details
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